Why Companies Fail to Earn the Social License To operate? Insights from the Extractive Sector In Tanzania
Bibliographic record
Abstract
In this paper we explore the actions of companies operating in the extractive sector in Tanzania to address the question why their actions failed to earn these companies the social license to operate (SLO). We used focus group discussions to collect extensive qualitative data at various sites where extractive activities have been taking place in the country. Our findings how that although companies have implemented a number of actions in the form of corporate social responsibility projects, paid compensations for land taken over, and paid local taxes, such actions have not succeeded in earning them the SLO. We found that the role of central government is pervasive in the whole SLO granting process even though it is the local community that grants it. Thus, companies alone are unlikely to earn SLO in situations where government policies, which companies have to follow, are perceived by communities to be inequitable. We recommend that companies engage more effectively communities neighboring natural resources extraction sites and remain sensitive to the broader local political milieu that could affect the granting of the SLO. We further suggest that the companies’ efforts need to be buttressed by appropriate government policies. Finally, we make suggestions for future research. Keywords: social license to operate; Tanzania, corporate social responsibility; extractive sector; community engagement.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.008 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".